If you asked me two months ago what data analysts do, I most definitely would have given you this clownish answer: “They analyze data.” That’s because I tend to act like a know-it-all, especially when asked seemingly simple questions. Then again, I didn’t know very much about data analysis back then.
Now, after finishing a data analytics bootcamp run by Level at Northeastern University, I can clearly see myself answering that very same question with many more words, a lot of more thought, and much, much more confidence. I should know; I have been asked to do so several times in job interviews.
So what do data analysts and data scientists do? In short, they provide quantitative support. They are the people that managers, developers, even CEOs go to when they need guidance. They are modern-day priests, and data is their holy scripture, and I will say that my bootcamp experience was successful in ordaining me.
Any department, from any organization, can benefit tremendously from consulting a data analyst or scientist. Given reliable data and enough time to work, they can come up with all kinds of valuable insights regarding anything, from identifying key-markers for potential sales growth, to pinpointing the exact temperature to set a thermostat. With data analytics skills, you can qualify for jobs like business analyst, health data analyst, financial analyst, or director of marketing.
Making those insights can be difficult, as it turns out, which is probably why Harvard Business Review called data scientist “the sexiest job of the 21st century.” Smart, after all, is the new sexy. But becoming smart simply means learning the right stuff, and figuring out what you are best at. Level taught me that, in the wide world of data analytics, there are many nuanced positions that each require specific skills. I believe that focusing on the skills you are comfortable with and interested in is the best way to find employment, in any field. So without further ado, here are my top tips for how to add data analytics to your skillset.
4 Tips to learning Data Analytics
1. The best way to learn data analysis tools is to use them.
In the morning we had lectures, in the afternoon we had labs. Each Level lab had something to do with the lecture. During labs is when the concepts we learned from the lectures gelled for me the most, personally, which is the case because I was applying them firsthand. Literally. For example, when we started out we were learning simple probability, which was something I thought I had mastered since I took two classes on it in college. But working with experimental results in Excel, R and Tableau made things like Bayes’ Theorem and type I & II errors really make sense to me, in ways they had never before.
Finding data can seem like an arduous task, but with the power of the Internet it is made simple. There are many free resources for large sets of data, including Data.gov, Kaggle, and Amazon Web Services, but what one guest lecturer told me was that he found that charities usually have large data sets just sitting around, and will usually be happy to let someone take a look at them. I am currently doing volunteer consulting work for a Boston-based charity organization, which has given me hours of valuable practice time, and may also make a difference.
2. Presenting on Topics Helps Solidify Lessons
Every other week during the Level course, we were given a project that we were told to give a presentation on. Some were individually based, and some were in groups. This, and other exercises we participated in, helped me in learning soft skills that will definitely come in handy throughout my career. Getting up and speaking in front of people was not a new experience for me, but interpreting an analysis of data to a live audience was.
One of the projects I worked on was that I looked at web analytics for my own blog, and worked out how consistent my writing schedule was for past years. Explaining the process and interpreting my results made me feel slightly nerdy, for having to tell strangers how many science-fiction short stories I had written, but I loved getting feedback because it gave me confidence, and it helped me refine my public-speaking skills. The number is 49 and still growing, if you must know.
3. Lecture is great, but field work is necessary
I thought the classroom part of the bootcamp was ideal for learning all aspects of being a data analyst or data scientist, except for one thing: gathering requirements. Learning what the problem is that you have to solve is an important part of the data analysis process, because each data analysis problem is unique, and it is difficult to develop that skill in a classroom setting. Most of the problems we worked on were textbook examples with foregone conclusions; there was no element of ambiguity about them. That is where the capstone project came in.
Everyone in the course got paired up with an organization who had a real data problem on their hands, and we were tasked with finding a solution to it, and presenting our results firsthand. The instructors advised us to ask our sponsors lots of questions about their data, and the problem that they needed solved. Doing that proved to be super useful for framing my approach to the project, so I highly recommend it if you are working on a data analysis project yourself.
For my capstone project, I worked with a large market research organization. Because I signed an NDA, I must keep certain aspects of my project a secret, but I will tell you that it had to do with survey data. I was given raw data that they collected from one of their surveys, and was asked to conduct a preliminary investigation as to what insights could be drawn from it. This gave me a chance to practice manipulating a large data set, and visualizing what trends I could find in it that I thought were insightful. Putting a presentation together, which is something data analysts constantly do, pulls together many skills that you really need feedback in order to work on, so practicing them in the field is crucial.
4. Learning with Peers is a Must
Having a motivated group of peers to navigate the curriculum material with was energizing, and felt unique to a bootcamp. The accelerated pace that we were learning at made me feel proud of what I was doing, and I could feel that the others were radiating that same sense of pride. If you choose not to learn data analytics in a classroom setting, I believe that you will be missing out on the inspiration, motivation, and feeling of mutual respect that you would have if you were to go through learning it with a group. Even if they come from just one other person, those things can go a long way, so I suggest making a friend who will learn data analytics with you.
From the beginning of the course, the Level instructors emphasized that our classmates were a) going to help us in learning the subject material and b) going to be a part of a shared network for a long time after graduation. I loved the feeling of solidarity that Level fostered between me and my classmates, and believe that it made it easier for me to learn the material. Maybe I got lucky, because my cohort at Level was fantastic, but I am sure that any group can find will provide you with at least some moral support to gain strength from.
After gaining analytical skills, I feel that I can view business problems from a more objective perspective. I hope that providing quantitative support is a part of my roles moving forward, because I would get to solve problems and communicate my ideas, which are things that I enjoy. I would like to work at a non-profit organization, because I would feel proud having my work make a difference.
I thoroughly enjoyed my experience with Level, and would emphatically recommend a bootcamp style course to anyone who is looking to learn data analysis skills in a short period of time. The bootcamp Level offers is rigorous, fast-paced, and challenging, but completing it was a life-changing experience for me.